Why CMO's Should Focus on the Financial Performance of Digital Marketing

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At MTA, Chitra creates research-based content that reflects the dynamics of the martech industry. She also lends her expertise to help plan and execute diverse campaigns, events & content strategies on the MTA platform, based on unique client needs. With over 15 years of experience in strategic marketing and communications, she has a great grasp on the way marketing professionals approach technology, their need to evolve and transform as marketers in the digital age, and the challenges therein. Specializing in Content Strategy, Digital Marketing and Loyalty Marketing; and having worked on both the marketer and the vendor side, Chitra has a knack for writing about martech in a way that simplifies this complex landscape for the end-reader, while still addressing the depth and layers of the subject. Chitra has studied media and communications at the London School of Economics and Political Science, UK, and worked at blue chip companies including Timken, Tata Sky and Procter & Gamble (P&G).

How much of your digital marketing spends are falling through the cracks with no accountability on return at all?

What percentage of marketing insights and reports are ever even read, forget acted upon, in your marketing organization?

How hard is it to connect the dots between the various marketing systems installed to really stick together a comprehensive picture about digital marketing performance across campaigns?

Are these questions that keep you up at night? Ken Gardner, 6-time entrepreneur, and presently Founder and CEO of conDati, a data science-powered analytics company, reminds us why the financial performance of digital marketing should be a priority for every CMO, and tells us why with big data and the cloud at their disposal, it needn’t keep them up at night anymore.

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1. What is the biggest challenge with marketing data analytics today? Is it the data per se or the ability to use the data?

“The problem with marketing analytics today, and the reason marketers struggle with turning their data into intelligence stems from 'data silos'. Even the most extensive of the integrated marketing automation platforms don’t cover all the functionality that’s available from the 7,000+ Martech systems now available. Every company with any web presence to speak of uses at least half a dozen different systems, and many companies (especially B2C) use dozens. These vendors all have different data schemas, different reporting systems, and different dashboard set-ups”.

So, companies have two choices:

A large-scale data integration project, which typically costs hundreds of thousands of dollars, takes two years (if it ever gets finished), requires 2-3 FTEs to maintain, and another 2-3 FTEs to create reports and analyze them.

Screen-scrape dashboards and reports from different systems into a set of spreadsheets – which takes 10-30 hours per week (or more!) of work time, is incomplete, and often suffers from manually-introduced errors. And the results are by definition obsolete by the time they are reviewed.

It’s a small wonder that so few marketing insights are even read, leave alone acted upon, since there is such a low degree of statistical confidence in what those insights say.

2. Why are CMOs struggling with connecting marketing to its financial/ business outcomes, even in this day and age where everything is 'trackable'?

From an analytics point of view, any solution should be able to deal with any data that can be put into a structured data format. Will that create a seamless brand experience? It’s unlikely. The variety of human experience is infinite, and infinity doesn’t lend itself well to being captured in a data table.

Everything is trackable but not by the same systems and hence the problem. When it comes to the financial performance of digital marketing campaigns – the revenue (or other goal completion) side of campaigns is typically captured in the system of record (Google Analytics, Adobe Analytics or the CRM). However, the plethora of available ad platform vendors, from Google, Facebook and Amazon on down, don’t share their ad rates with each other. For example, Google Analytics does not capture what the customer has paid for Facebook ads (or Amazon ads). So, if Marketing wants better data to prove ROI to the Board, they need a way to get all their cost and revenue data from all the digital systems that collect this type of information into a single and unified data asset. Today, for example, lining up revenue from Google Analytics with costs from a dozen other platforms, so both revenue and costs are attributed accurately to individual campaigns, is effectively impossible.

3. Data science is a scary word for most marketers. What can a CMO do to foster a more data-backed decision-making culture in their teams? What is the opportunity for marketing analytics vendors here to help drive adoption of analytics for decision-making?

Data science, machine learning and AI are nothing but a means to an end. There is nothing particularly new in the machine learning algorithms: most of what is being done today could have been done a decade or more ago – it just would have cost a million dollars a month in storage and compute power. With cloud storage and high-performance processing priced by the second from Amazon, Microsoft, Google, et al., crunching vast amounts of data is now very affordable.Integrating the data, figuring out which algorithms to apply to which problems, and delivering visualizations that work are the tricky parts.

Make those results as easy for humans to ingest, understand, share, and act on as possible. If analytics systems are not as intuitive to work with as a Netflix account, they won’t be used.

Deliver insights that can be demonstrated to improve the revenue line. If the systems also save cost and time, then they have the potential to become truly disruptive.

4. Tell us about data visualization and storytelling. What is it, and how can marketing teams incorporate it into their analytics to make it more user-friendly?

Visualization is the fastest and most effective way for humans to absorb complex data. The key is to figure out what kind of visualization best maps to the data so the viewer can understand the story at a glance – AND obtain additional detail without having to leave the current visualization. Think Apple, Amazon, Netflix etc.. This kind of instant understanding needs to make its way into business solutions in at least three ways:

In the last decade or so, consumer applications have been far ahead of business applications in their ability to convey lots of complex data and help the user to understand her choices and act on them.

Every individual visualization ought to tell its story instantly. If the goal is to compare the performance of many different items in the same category – say, digital marketing campaigns – then a green-yellow-red heatmap gives instant understanding of what is working and what is not.

Every visualization should give instant access to the next level of information. Think about the heatmap example: on a screen of green, humans will naturally gravitate to the red tile. That tile should be not just a representation of the data; it should also be a navigable element that brings the user directly to the information that helps explain why this tile (i.e., the campaign) is red (i.e., underperforming).

Visualizations also need to work to build a complete picture when used together. Every marketing team loathes the dreaded quarterly business review (QBR): the day(s)-long deep dive into marketing performance and its business results. With cloud-based machine learning and good visualizations, the entire QBR story can be told in 10-15 minutes – little enough time to review it every single day in the marketing stand-up.

5. Big data is something that is perceived as the CTOs domain because it involves all of the data from all the (non-marketing) functions. How can CMOs work better and closer with CTOs to get more from organization-wide big data?

Successful innovation requires IT and Marketing collaboration, and common solutions include: The SaaS model arose because IT couldn’t deliver the solutions that marketing and other groups needed in any timeframe that actually helped.

This question might be better posed to a relationship therapist than to an analytics vendor.

Thanks to Moore’s Law, it became feasible and then cost-advantaged for Marketing to acquire their solutions from innovative and opportunistic third-party vendors. This behavior by Marketing then caused all kinds of headaches for IT, including costs, inconsistent data schema, redundant solutions and vendors, and probably most importantly, security. So, Marketing and IT have trust issues, dependability issues, accountability issues, and blame issues to go along with fundamental and appropriate cultural and personality differences.

Corporations have been struggling for almost two decades to put enterprise-wide data governance in place that supports the business, doesn’t add too much cost or infrastructure, and preserves data/ cyber security.

Reach enterprise agreement on the minimum-security requirements to work with any cloud vendor.

Reach enterprise agreement on confidentiality requirements for different kinds of data.

Demonstrate how Marketing generates better business results with more and better data.

Embed IT personnel into functional units – and less frequently, vice versa.

With more and more products/ services becoming data-enabled, IT and Marketing need to work in tandem to specify, deliver, promote and evolve those products and services.

6. Do you think B2B marketers can do more with predictive analytics and data-driven marketing? What are some of the practical things CMO’s can do by using data more intelligently for business outcomes?

When you collect this much data (time series data, that is), and keep and use all of it (instead of 1-3%, per Forbes), it turns out that you can do some really cool things, and one of those things is to predict the future, with pretty good accuracy. Practically speaking, some examples of what marketers can now do:

Understand the seasonality that affects every business, whether it’s time of day, day of week, time of year, or the combination of all of them. Once that’s understood, you can know with some precision what your revenue should be right now, in time to fix anomalies.

If a new marketing campaign is going to fail, it’s typically going to fail in the first 30 minutes (B2C) or first 1-2 days (B2B). If you catch it that quickly, you might be able to fix it. But how do you know it’s failing or succeeding if you don’t know what it is supposed to do (in the statistical sense, not in the business plan sense)? Predictive analytics can tell you.

All advertising loses its punch over time: the decay curves of its efficacy can be calculated. The ideal moment to pull any given ad is just before it ceases to be effective – which predictive analytics can tell you in a way that nothing else can.

As (analytics) technology proves itself out, Marketing leaders should not invest in science projects, moon shots, or any solution that promises too much.

conDati is a provider of analytics for digital marketing that helps companies drive value from their cloud-based marketing applications and improve the ROI from digital marketing. The company’s cloud-based data science application delivers the next generation of marketing analytics, creating a single unified data asset from siloed martech systems.